We consider a pull-based status update system consisting of a finite-state Markov source, an energy-harvesting-enabled transceiver, and a sink. Both data and command channels are error-prone. The sink is interested in real-time tracking of the source and sends commands for updates to the transceiver. However, due to noise in the command channel, the transceiver may experiencefalse alarmsormiss-detections. We study the problem of minimizing the long-term time average of a (generic) distortion subject to a constraint on the average number of commands. Due to the pull-based nature of the system, the controller (i.e., sink) has only partial observability of the source state and the available energy at the transceiver. Therefore, we model the problem as a constrained partially observable Markov decision process (C-POMDP), which is then cast as a constrained belief-MDP (C-belief-MDP) problem. The infinite belief-state space makes solving the C-belief-MDP difficult. Thus, we effectively truncate the belief-state space and formulate a finite-state C-belief-MDP problem. We develop a stationary randomized policy (SRP) to solve the problem by employing Lagrangian relaxation, the relative value iteration algorithm (RVIA), and bisection search. Due to the curse of dimensionality in deriving the SRP, we also develop a low-complexity policy applying the drift-plus-penalty method, which transforms the C-belief-MDP problem into a sequence of per-slot optimization problems. Simulation results show the effectiveness of the proposed policies and their superiority compared to baseline policies.

We consider a status update system consisting of one source, one server, and one sink. The source generates packets according to a Poisson process and the packets are served according to a generally distributed service time. We consider a system with a capacity of one packet, i.e., there is no waiting buffer in the system, and model it as an M/G/1/1 queueing system. We introduce a probabilistically preemptive packet management policy and calculate the moment generating functions (MGFs) of the age of information (AoI) and peak AoI (PAoI) under the policy. According to the probabilistically preemptive policy, when a packet arrives, the possible packet in the system is replaced by the arriving packet with a fixed probability. Numerical results show the effectiveness of the packet management policy.

We consider an Internet of Battlefield Things (IoBT) system consisting of multiple devices that want to securely communicate with each other during a mission in the presence of an adversary with unbounded computational power. The adversary has complete access to listen/read the ciphertext without tampering with the communication line. We provide an unconditionally secure encryption scheme to exchange messages among devices in the system. The main idea behind the scheme is to provide secret keys to exchange messages using a random binary matrix that is securely shared among all the devices, and pair-wise random secret keys established between each pair of devices attempting to communicate before the mission. The scheme is implemented by using finite group modular addition. We show that the scheme is absolutely semantically secure, i.e., the scheme guarantees that an adversary with unbounded computational power cannot get even one bit of information about a message, except for an exponentially small probability in a security parameter. Besides that, we show that even if the random binary matrix is revealed to the adversary, the provided scheme is computationally secure against the key recovery attack.

We address the problem of real-time remote tracking of a partially observable Markov source in an energy harvesting system with an unreliable communication channel. We consider both sampling and transmission costs. Different from most prior studies that assume the source is fully observable, the sampling cost renders the source partially observable. The goal is to jointly optimize sampling and transmission policies for two semantic-aware metrics: i) a general distortion measure and ii) the age of incorrect information (AoII). We formulate a stochastic control problem. To solve the problem for each metric, we cast a partially observable Markov decision process (POMDP), which is transformed into a belief MDP. Then, for both AoII under the perfect channel setup and distortion, we express the belief as a function of the age of information (AoI). This expression enables us to effectively truncate the corresponding belief space and formulate a finite-state MDP problem, which is solved using the relative value iteration algorithm. For the AoII metric in the general setup, a deep reinforcement learning policy is proposed. Simulation results show the effectiveness of the derived policies and, in particular, reveal a non-monotonic switching-type structure of the real-time optimal policy with respect to AoI.

We provide an encryption protocol for storing highly confidential data of a user on a public cloud storage. We show that the protocol provides unconditional security. More specifically, we prove that the protocol is semantically secure against an all-powerful adversary with unbounded computational power and storage capacity who has complete access to the communication line. The provided protocol is very simple, it is implemented by exploiting finite group modular addition and XOR operations. The protocol provides a high security gain defined as the ratio of the amount of data (in bits) that can be securely stored on the public cloud and the number of secret bits required to be stored at the user end. As an illustrative example, according to the protocol, by using 40.2 Terabytes (TBs) of secret bits, a user is able to store 5.9 ×108TBs of data on a public storage with guaranteed everlasting security.

We study the problem of remote tracking in an energy-harvesting enabled status update system consisting of an information source, a sampler, a transmitter, and a monitor. The information source is modeled as a finite-state Markov chain. The sampler samples the source, and the transmitter transmits the taken samples to the monitor. We consider both sampling and transmission costs, and thus, the source is not fully observable. The primary objective is to determine the optimal joint sampling and transmission policies based on a goal-oriented metric, defined by a generic distortion function. We first formulate a stochastic optimization problem and cast it into a partially observable Markov decision process (POMDP) problem. Subsequently, we employ the notion of belief state and characterize the belief space through the age of information (AoI) to convert the problem into a finite-state MDP problem, which is then solved via the relative value iteration algorithm. We also explore different estimation strategies at the monitor and examine their impact on the system performance. The simulation results show the effectiveness of the derived policy and reveal that, depending on the source dynamic, the choice of estimation strategy itself can significantly influence the overall performance.

Deep neural networks (DNNs) have been widely used in recent years for wireless communication applications, including channel estimation. However, DNN performs well for environments (data) that it has been trained for. However, it is still challenging to work in a non-stationary system where the statistical characteristics of the environment change with time. This paper introduces an online adaptation approach that allows the neural network to dynamically change to cope with the non-stationary channel estimation. The idea is to use the existing detected packets to continuously update the weights of the DNN. Simulation results show that our approach can significantly improve the performance of the DNN in non-stationary environments whereas concurrent state-of-the-art algorithms fail.

In the bounded storage model introduced by Maurer, the adversary is computationally unbounded and has a bounded storage capacity. In this model, perfect secrecy is guaranteed by using a publicly available random string whose length is larger than the adversary storage capacity. The protocol proposed by Maurer is simple, from the perspective of implementation, and efficient, from the perspective of the initial secret key size and random string length. However, he provided the proof of the security for the case where the adversary can access a constant fraction of the random string and store onlyoriginal bitsof the random string. In this paper, we provide a new proof of the security of the protocol proposed by Maurer for the general bounded storage model, i.e., the adversary can access all bits of the random string, and store the output of any Boolean function on the string. We reaffirm that the protocol isabsolutely semantically securein the general bounded storage model.

We consider a multi-source relaying system where independent sources randomly generate status update packets which are sent to the destination with the aid of a relay through unreliable links. We develop transmission scheduling policies to minimize the weighted sum average age of information (AoI) subject to transmission capacity and long-run average resource constraints. We formulate a stochastic control optimization problem and solve it using a constrained Markov decision process (CMDP) approach and a drift-plus-penalty method. The CMDP problem is solved by transforming it into an MDP problem using the Lagrangian relaxation method. We theoretically analyze the structure of optimal policies for the MDP problem and subsequently propose a structure-aware algorithm that returns a practical near-optimal policy. Using the drift-plus-penalty method, we devise a near-optimal low-complexity policy that performs the scheduling decisions dynamically. We also develop a model-free deep reinforcement learning policy for which the Lyapunov optimization theory and a dueling double deep Q-network are employed. The complexities of the proposed policies are analyzed. Simulation results are provided to assess the performance of our policies and validate the theoretical results. The results show up to 91% performance improvement compared to a baseline policy.

We study status updating under two-way delay in a system consisting of a sampler, a sink, and a controller residing at the sink. The controller drives the sampling process by sending request packets to the sampler. Upon receiving a request, the sampler generates a sample and transmits the status update packet to the sink. Transmissions of both request and status update packets encounter random delays. We develop optimal control policies to minimize the average age of information (AoI) using the tools of Markov decision processes in two scenarios. We begin with the system having at most one active request, i.e., a generated request for which the sink has not yet received a status update packet. Then, as the main distinctive feature of this paper, we initiate pipelined-type status updating by studying a system having at most two active requests. Furthermore, we conduct AoI analysis by deriving the average AoI expressions for the Zero-Wait-1, Zero-Wait-2, and Wait-1 policies. According to the Zero-Wait-1 policy, whenever a status update packet is delivered to the sink, a new request packet is inserted into the system. The Zero-Wait-2 policy operates similarly, except that the system can hold two active requests. According to the Wait-1 policy, whenever a status update packet is delivered to the sink, a new request is sent after a waiting time which is a function of the current AoI. Numerical results illustrate the performance of each status updating policy under varying system parameter values.

We consider a multi-source status update system consisting of multiple independent sources, one server, and one sink. The packets of the sources are generated according to Poisson processes and served according to a generally distributed service time. We consider a system with no waiting buffer and model it as a multi-source M/G/1/1 queueing model. We introduce a source-aware preemptive packet management policy and subsequently derive the moment generating functions (MGFs) of the age of information (AoI) and peak AoI of each source. According to the policy, when a packet arrives, the possible packet of the same source in the system is replaced by the fresh packet. Simulation results show the performance of the packet management policy.

We consider a multi-source status update system, where each source generates status update packets according to a Poisson process which are then served according to a generally distributed service time. For this multi-source M/G/1/1 queueing model, we consider a self-preemptive packet management policy and derive the moment generating functions (MGFs) of the age of information (AoI) and peak AoI of each source. According to the policy, an arriving fresh packet preempts the possible packet of the same source in the system. Furthermore, we derive the MGFs of the AoI and peak AoI for the globally preemptive and non-preemptive policies, for which only the average AoI and peak AoI have been derived earlier. Finally, we use the MGFs to derive the average AoI and peak AoI in a two-source M/G/1/1 queueing model under each policy. Numerical results show the effect of the service time distribution parameters on the average AoI. The results also highlight the importance of higher moments of the AoI.

We consider a status update system where multiple sensors communicate timely information about various random processes to a sink. The sensors share orthogonal sub-channels to transmit such information in the form of status update packets. A central controller can control the sampling actions of the sensors to trade-off between the transmit power consumption and information freshness which is quantified by the Age of Information (AoI). We jointly optimize the sampling action of each sensor, the transmit power allocation, and the sub-channel assignment to minimize the average total transmit power of all sensors, subject to a maximum average AoI constraint for each sensor. To solve the problem, we develop a dynamic control algorithm using the Lyapunov drift-plus-penalty method and provide optimality analysis of the algorithm. According to the Lyapunov drift-plus-penalty method, to solve the main problem, we need to solve an optimization problem in each time slot which is a mixed integer non-convex optimization problem. We propose a low-complexity sub-optimal solution for this per-slot optimization problem that provides near-optimal performance and we evaluate the computational complexity of the solution. Numerical results illustrate the performance of the proposed dynamic control algorithm and the performance of the sub-optimal solution for the per-slot optimization problem versus the different parameters of the system. The results show that the proposed dynamic control algorithm achieves more than$60~\%$saving in the average total transmit power compared to a baseline policy.

We consider a multi-source relaying system where the sources independently and randomly generate status update packets which are sent to the destination with the aid of a buffer-aided relay through unreliable links. We formulate a stochastic optimization problem aiming to minimize the sum average age of information (AAoI) of sources under per-slot transmission capacity constraints and a long-run average resource constraint. To solve the problem, we recast it as a constrained Markov decision process (CMDP) problem and adopt the Lagrangian method. We analyze the structure of an optimal policy for the resulting MDP problem that possesses a switching-type structure. We propose an algorithm that obtains a stationary deterministic near-optimal policy, establishing a benchmark for the system. Simulation results show the effectiveness of our algorithm compared to benchmark algorithms.

We consider a multi-source status update system in which status updates are transmitted as packets containing the measured value of the monitored process and a time stamp representing the time when the sample was generated. The packets of each source are generated according to a Poisson process and served according to an exponentially distributed service time. We assume that the received status update packets need further processing before being used (hence, computation intensive). This is mathematically modeled by an additional server at the sink. The sink server serves the packets according to an exponentially distributed service time. We introduce two packet management policies, a preemptive policy and a blocking policy, and derive the moment generating function (MGF) of the AoI of each source under the both policies. In the both policies, the system can contain at most two packets, one at the transmitter server and one at the sink server. In the preemptive policy, a new arriving packet preempts any possible packet that is currently under service regardless of the packet’s source index. In the blocking policy, when a server is busy at the arrival instant of a packet, the arriving packet is blocked and cleared. We assume that the same preemptive/blocking policy is employed in both the transmitter and sink server. Numerical results are provided to assess the results.

We study the information freshness under three different source aware packet management policies in a status update system consisting of two independent sources and one server. The packets of each source are generated according to the Poisson process and the packets are served according to an exponentially distributed service time. We derive the average age of information (AoI) of each source using the stochastic hybrid systems (SHS) technique for each packet management policy. In Policy 1, the queue can contain at most two waiting packets at the same time (in addition to the packet under service), one packet of source 1 and one packet of source 2. When the server is busy at an arrival of a packet, the possible packet of the same source waiting in the queue (hence, source-aware) is replaced by the arrived fresh packet. In Policy 2, the system (i.e., the waiting queue and the server) can contain at most two packets, one from each source. When the server is busy at an arrival of a packet, the possible packet of the same source in the system is replaced by the fresh packet. Policy 3 is similar to Policy 2 but it does not permit preemption in service, i.e., while a packet is under service all new arrivals from the same source are blocked and cleared. Numerical results are provided to assess the fairness between sources and the sum average AoI of the proposed policies.

We consider a status update system consisting of two independent sources, one server, and one sink. The packets of different sources are generated according to the Poisson process and the packets are served according to an exponentially distributed service time. We consider the following packet management policy. When the system is empty, any arriving packet immediately enters the server; when the server is busy, a packet of a source waiting in the queue is replaced if a new packet of the same source arrives. We derive the average age of information (AoI) of the considered M/M/1 queueing model by using the stochastic hybrid systems (SHS) technique. Numerical results are provided to show the effectiveness of the proposed policy.

Information freshness is crucial in a wide range of wireless applications where a destination needs the most recent measurements of a remotely observed random process. In this paper, we study the information freshness of a single-server multi-source M/M/1 queueing model under a first-come first-served (FCFS) serving policy. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the multi-source M/M/1 queueing model. Simulation results are provided to validate the derived exact expression for the average AoI.

Freshness of status update packets is essential for enabling services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of single-server multi-source queueing models under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the case with exponentially distributed service time, i.e., for a multi-source M/M/1 queueing model. Moreover, we derive three approximate expressions for the average AoI for a multi-source M/G/1 queueing model having a general service time distribution. Simulation results are provided to validate the derived exact average AoI expression, to assess the tightness of the proposed approximations, and to demonstrate the AoI behavior for different system parameters.

In the context of the next generation wireless networks, freshness of status update packets is essential for enabling the services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of a multi-source M/G/1 first-come first-served (FCFS) queueing model, where each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated using the average age of information (AoI). To this end, we derive a closed-form expression for the average AoI of each source. As particular cases of our general expressions, we also derive closed-form expressions of the average AoI for both multi-source M/M/1 and single-source M/G/1 queueing models.

We consider the problem of channel estimation in hybrid transceiver architectures operating in millimeter wave (mmWave) band. Due to the dynamic features of the environment and the sensitivity of mmWave bands to blockage and deafness, it is important to estimate mmWave channels with a low complexity and high performance algorithm. In this regard, we exploit the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse signal reconstruction in frequency domain. In order to solve the estimation problem, we propose a multi-stage based low complexity algorithm. Simulation results show that the proposed algorithm significantly reduces the computational complexity while preserving the quality of the estimation.

In this paper, we propose a cross layer energy efficient resource allocation and remote radio head (RRH) selection algorithm for heterogeneous traffic in power domain-non-orthogonal multiple access (PD-NOMA) based heterogeneous cloud radio access networks (H-CRANs). The main aim is to maximize the EE of the elastic users subject to the average delay constraint of the streaming users and the constraints, RRH selection, subcarrier, transmit power, and successive interference cancellation. The considered optimization problem is non-convex, NP-hard, and intractable. To solve this problem, we transform the fractional objective function into a subtractive form. Then, we utilize successive convex approximation approach. Moreover, in order to increase the processing speed, we introduce a framework for accelerating the successive convex approximation for low complexity with the Lagrangian method on graphics processing unit. Furthermore, in order to show the optimality gap of the proposed successive convex approximation approach, we solve the proposed optimization problem by applying an optimal method based on the monotonic optimization. Studying different scenarios show that by using both PD-NOMA technique and H-CRAN, the system energy efficiency is improved.

In this paper, the performance of fifth generation (5G) cellular networks under two promising technologies, namely dual connectivity and coordinated multi-point transmission (CoMP) is investigated. In this regard, a joint access and fronthaul radio resource allocation for a two tier downlink heterogeneous cloud radio access network (H-CRAN) is formulated. The main aim of the proposed problem formulation is to maximize the system energy efficiency (EE) by using both millimeter wave (mmW) and micro wave (μW) links in access and fronthaul. The proposed optimization is a mixed integer nonconvex problem with a high computational complexity solution. Therefore, existing convex optimization methods can not be used directly to solve this problem. To solve this issue, an iterative algorithm based on a successive convex approximation (SCA) approach with low complexity is exploited. As the numerical results show, via dual connectivity and CoMP EE is improved by approximately 90% compared to using only μW subcarriers.

In this paper, fifth-generation (5G) cellular networks under three promising technologies, namely, dual connectivity, coordinated multi-point transmission (CoMP), and power domain non orthogonal multiple access (PD-NOMA) are investigated. The main aim is to maximize the downlink energy efficiency (EE) by using both millimeter wave (mmW) and micro wave (μW) links in access and fronthaul, while employing CoMP and PD-NOMA. In this regard, joint access and fronthaul radio resource allocation for a downlink heterogeneous cloud radio access network is considered. The proposed optimization is a mixed integer non-convex problem with a high computational complexity solution, and hence, the alternate search method based on a successive convex approximation approach using fractional programming is exploited. Furthermore, the convergence of the proposed iterative resource allocation method is proved and its computational complexity is investigated. As the numerical results show, via dual connectivity through receiving signals from both mmW and μW transmitters, the system EE is improved by approximately 50%, in contrast to using only μW subcarriers (e.g., as in local thermal equilibrium). In addition, by applying both PD-NOMA and CoMP technologies on the μW subcarriers, the EE of the system increases by approximately 45%.

In this paper, the tradeoff among the energy efficiency, fairness, harvested energy, and system sum rate is studied. In this regard, various fairness methods, namely, max-min fairness, proportional fairness, and minimum delay potential fairness in power-domain non-orthogonal multiple access-based heterogeneous cellular networks are investigated. In order to perform successive interference cancellation (SIC), we use two ordering approaches and compare their performance. To this end, we propose joint subcarrier and power allocation algorithms to achieve fair energy efficient resource allocation for each fairness method and SIC ordering. Since the proposed optimization problems are non-convex and intractable, the existing methods to solve the convex problems could not be directly used. To overcome this difficulty, an iterative algorithm based on successive convex approximation is used. Moreover, to show the optimality gap of the proposed solution method, an optimal approach based on the monotonic optimization is applied in which we first transform each of the proposed optimization problems into a monotonic optimization problem of canonical form, and then, we obtain the optimal solution of each problem, which coincides with the optimal solution of the original non-convex problem. We finally study the performance of the proposed schemes using simulations for different values of the system parameters.

In this paper, a new approach for multiple access (MA) in fifth generation (5G) of cellular networks called power domain sparse code multiple access (PSMA) is proposed. In PSMA, we adopt both the power domain and the code domain to transmit multiple users' signals over a subcarrier simultaneously. In such a model, the same sparse code multiple access (SCMA) codebook can be used by multiple users where, for these users, power domain non-orthogonal multiple access (PD-NOMA) technique is used to send signals non-orthogonally. Although different SCMA codebooks are orthogonal and produce no interference over each other, the same codebook used by multiple users produces interference over these users. We investigate the signal model as well as the receiver and transmitter of the PSMA method. To evaluate the performance of PSMA, we consider a single cell with multiple users. In this case, our design objective is to maximize the system sum rate of the network subject to some system level and QoS constraints such as transmit power constraints. We formulate the proposed resource allocation problem as an optimization problem and solve it by successive convex approximation (SCA) techniques. Finally, the effectiveness of the proposed approach is investigated using numerical results.